Penelope v0.4.0 Penelope.ML.Feature.StackVectorizer

This vectorizer horizontally stacks the results of a sequence of inner vectorizers applied to an incoming feature matrix. This is analogous to the behavior of the FeatureUnion component in sklearn.

Example:

  features = [
    {:count_vectorizer, []},
    {:regex_vectorizer, [regexes: [~r/ed$/, ~r/ing$/]]},
  ]
  pipeline = [
    {:ptb_tokenizer, []},
    {:feature_stack, features},
    {:svm_classifier, [c: 2.0]},
  ]
  Penelope.ml.pipeline.fit(%{}, x, y, pipeline)

Link to this section Summary

Functions

imports parameters from a serialized model

exports a runtime model to a serializable data structure

fits each of the configured inner vectorizers

transform a list of feature vectors using the inner featurizers and stack the results into a single vector per sample

Link to this section Functions

Link to this function compile(params)
compile(params :: [map()]) :: [{atom(), any()}]

imports parameters from a serialized model

Link to this function export(model)
export(model :: [{atom(), any()}]) :: [map()]

exports a runtime model to a serializable data structure

Link to this function fit(context, x, y, features)
fit(context :: map(), x :: [any()], y :: [any()], features :: [{String.t() | atom(), any()}]) :: [{atom(), any()}]

fits each of the configured inner vectorizers

Link to this function transform(model, context, x)
transform(model :: [{atom(), any()}], context :: map(), x :: [any()]) :: [Penelope.ML.Vector.t()]

transform a list of feature vectors using the inner featurizers and stack the results into a single vector per sample